Boston useR: Deploying a Vehicle Score Model to Predict When a Car Will Sell

Deploying a Vehicle Score Model to Predict When a Car Will Sell: Plumber and SageMaker

Abstract:
At some point in the car shopping process, over 70% of car shoppers interact with a Cox Automotive brand. For our data science teams, this means we have access to tremendous data for building models to deliver business insights and data products. However, delivering data science models that could be used in real-time at the scale required by Cox Automotive was a challenge. Many methods for model delivery limit the modeling approaches, or require substantial rework by the engineering teams. By leveraging Plumber, and working with Engineering teams to implement using the AWS SageMaker bring-your-own-algorithm feature, we were able to decouple the development of a statistical model from the engineering specifications for stability and reliability. This allowed each team to focus on their core skill sets, and fostered collaboration between data science and engineering teams.

In addition, I’ll also talk about the twists and turns of integrating data science teams across three different businesses, locations, and software skill sets, all the while migrating our data and resources to the AWS cloud.

About the Speaker
John Stanton-Geddes is a Manager for the Decision Science team at Cox Automotive, where he started as a Data Scientist in 2014. Previously, he worked in ecological genomics at the University of Vermont, where satisfied a life-long goal of getting paid to crush ants. He has a PhD in Evolutionary Biology and Ecology with a minor in Statistics from the University of Minnesota, where he contributed to novel techniques for life-history analysis, and a BA from Amherst College. He's a co-founder and organizer of the Burlington Data Scientists Meetup group. He used to have free-time, but now happily spends it chasing his kids around the soccer field and up mountain trails.

Location:
WeWork St James

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